Authentication Performance Analysis of Acoustic-Based Ear Biometrics System Using Audible Signals

Abstract

A study of an acoustic-based ear biometrics system for individual identification using audible signals was undertaken. In this era of too much network connectivity, an increase in cybercrimes has led to the loss of key information or leakage of it. Experts have therefore continued to work hard to protect and secure important data from being stolen, lost, leaked, or tempered with. The use of biometric systems is one of the methods that many have adopted to provide the needed security and protection of data. The authors in this study proposed an acoustics-based ear biometric system for the identification of individuals while taking into consideration the dynamics of real-time data capture and using real persons for user authentication applications. The system has been developed in the MATLAB/SIMULINK language which supports dynamic real-time data capture. The results of simulation experiments showed that with proper experimentation and threshold calibration, it is possible to develop acoustics-based authentication systems that can identify individuals correctly and with 100% recognition accuracy. Depending on the human subjects under study, the threshold cosine similarity setting may vary between 0.2 and 0.4. However, this variation is offset by the enrollment procedure deployed in practice. Furthermore, trend analysis using moving average analysis revealed the possibility that a false acceptance is equally likely even though 100% recognition accuracy was attained.